no code implementations • 23 Dec 2024 • Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation.
no code implementations • 22 Dec 2024 • Kai Zheng, Qingfeng Sun, Can Xu, Peng Yu, Qingwei Guo
We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs.
1 code implementation • 1 Aug 2024 • Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, JianGuang Lou, QIngwei Lin, Ping Luo, Saravan Rajmohan
Moreover, to increase the difficulty diversity of generated planning tasks, we propose a bidirectional evolution method, Bi-Evol, that evolves planning tasks from easier and harder directions to synthesize a task set with a smoother difficulty curve.
no code implementations • 15 Jul 2024 • Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, QIngwei Lin, JianGuang Lou, Shifeng Chen, Yansong Tang, Weizhu Chen
In this paper, we introduce Arena Learning, an innovative offline strategy designed to simulate these arena battles using AI-driven annotations to evaluate battle outcomes, thus facilitating the continuous improvement of the target model through both supervised fine-tuning and reinforcement learning.
1 code implementation • 18 Aug 2023 • Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, JianGuang Lou, Chongyang Tao, Xiubo Geng, QIngwei Lin, Shifeng Chen, Yansong Tang, Dongmei Zhang
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning.
Ranked #52 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
3 code implementations • 14 Jun 2023 • Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #6 on
Code Generation
on CodeContests
1 code implementation • 26 Apr 2023 • Kunzhe Song, Qingfeng Sun, Can Xu, Kai Zheng, Yaming Yang
To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation.
4 code implementations • 24 Apr 2023 • Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
1 code implementation • 10 Nov 2022 • Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, QIngwei Lin
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
no code implementations • NAACL 2022 • Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
1 code implementation • ACL 2022 • YuFei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, Xiubo Geng, Daxin Jiang
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
no code implementations • ACL 2022 • Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
no code implementations • IJCNLP 2019 • Shengli Sun, Qingfeng Sun, Kevin Zhou, Tengchao Lv
Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available.